Inter-person occlusion and depth ambiguity make estimating the 3D poses of
monocular multiple persons as camera-centric coordinates a challenging problem.
Typical top-down frameworks suffer from high computational redundancy with an
additional detection stage. By contrast, the bottom-up methods enjoy low
computational costs as they are less affected by the number of humans. However,
most existing bottom-up methods treat camera-centric 3D human pose estimation
as two unrelated subtasks: 2.5D pose estimation and camera-centric depth
estimation. In this paper, we propose a unified model that leverages the mutual
benefits of both these subtasks. Within the framework, a robust structured 2.5D
pose estimation is designed to recognize inter-person occlusion based on depth
relationships. Additionally, we develop an end-to-end geometry-aware depth
reasoning method that exploits the mutual benefits of both 2.5D pose and
camera-centric root depths. This method first uses 2.5D pose and geometry
information to infer camera-centric root depths in a forward pass, and then
exploits the root depths to further improve representation learning of 2.5D
pose estimation in a backward pass. Further, we designed an adaptive fusion
scheme that leverages both visual perception and body geometry to alleviate
inherent depth ambiguity issues. Extensive experiments demonstrate the
superiority of our proposed model over a wide range of bottom-up methods. Our
accuracy is even competitive with top-down counterparts. Notably, our model
runs much faster than existing bottom-up and top-down methods.

本论文提出了一种新的统一模型，通过结构化 2.5D 姿势估计和端到端几何感知深度推理方法相结合，解决了估计单目多人 3D 姿势以摄像机为中心坐标的问题，相比于现有的自顶向下和自底向上方法，我们的模型在准确性和速度方面都表现出色。